code: def is_distributed():
      """
      Check if distributed training is initialized.
      """
      global accelerate  # pylint: disable=global-statement
      if not accelerate:
          accelerate = Accelerator()
      return dist.is_available() and dist.is_initialized()
  
file_path: axolotl/src/axolotl/utils/distributed.py
imports: import os
  import pickle
  from contextlib import contextmanager
  import torch
  import torch.distributed
  from accelerate import Accelerator
label_desc: Use image node_id 2 for calling a global function with example usage: is_distributed() and returns: unknown